Spatio-temporal electrical grid emission factors effects on calculated GHG emissions of buildings in mixed-grid environments
Bibliographic record
Abstract
This study compares the calculated greenhouse gas (GHG) emissions of buildings using two different methodologies in mixed-grid environments. Simulations were conducted using virtual models of 25 buildings and actual meteorological data over 2016–2018. The “Annual Method” using yearly average emission factors and the “Hourly Method” using consumption-based hourly emission factors were used to calculate GHG emissions. The study found that the hourly method provided a more accurate representation of GHG emissions, especially during peak grid demand. Furthermore, the study recommends using a zonal approach to building codes in terms of electrical grids similar to climate zones in current codes and standards while also prioritizing building types with the largest potential for emissions reductions. A case study in Ontario, Canada found that electrification via heat pump always results in GHG savings independent of year, building model, and city if keeping the calculation method the same between fuel-switching models. Future research is needed to improve the accuracy of GHG emissions calculations and understand the relationship between electrical load and GHG emissions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".